DOH_ztest
Nhung
2022-09-23
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✔ ggplot2 3.4.0 ✔ purrr 0.3.4
## ✔ tibble 3.1.6 ✔ dplyr 1.0.8
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()m <- read_csv("DOH.csv")## Rows: 9867 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Post_oldid, date, state
## dbl (29): Real_ID, COVID_Post, RiskFactor_2_CTA, SocialDisparities_3_CTA, De...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.m## # A tibble: 9,867 × 32
## Post_oldid Real_ID date COVID_Post RiskFactor_2_CTA SocialDisparities_3_…
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1 2069 9/22/20 1 88 88
## 2 2 2070 8/27/20 1 88 88
## 3 3 2071 12/29/20 1 88 88
## 4 4 2072 9/29/20 1 88 88
## 5 5 2073 10/6/20 1 88 88
## 6 6 2074 12/31/20 1 88 88
## 7 7 403 4/2/20 1 88 88
## 8 8 5425 12/1/20 1 88 88
## 9 9 5842 12/20/20 1 88 88
## 10 10 2075 3/5/20 1 88 88
## # … with 9,857 more rows, and 26 more variables: Debunk_4_CTA <dbl>,
## # UncertaintyReduction_5_CTA <dbl>, Testing_6_CTA <dbl>, Vaccine_7_CTA <dbl>,
## # Mental_8_YesNo <dbl>, Emotion_9_Number <dbl>, Praise_10_Number <dbl>,
## # Pop_11_Number <dbl>, Language_12_Number <dbl>, Action_13_CTA <dbl>,
## # Positive_14a_Number <dbl>, Negative_14b_Number <dbl>,
## # Individual_14c_Number <dbl>, Collective_14d_Number <dbl>,
## # `State_ recode` <dbl>, Gov_poli <dbl>, Trifecta <dbl>, …table(m$RiskFactor_2_CTA)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 19 300 268 212 220 105 69 55 35 12 22 8 1 2 4 2
## 16 18 25 26 88
## 5 2 1 1 8524table(m$SocialDisparities_3_CTA)##
## 0 1 2 3 4 5 6 7 8 11 15 26 88
## 12 111 25 9 8 1 2 3 1 1 2 1 9691table(m$Debunk_4_CTA)##
## 0 1 2 3 4 5 6 7 8 9 11 15 88
## 54 180 86 40 44 11 5 8 3 1 1 2 9432table(m$UncertaintyReduction_5_CTA)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 871 4561 1733 941 473 306 199 102 68 24 61 22 3 4 4 5
## 16 17 18 25 26 88
## 5 1 4 1 1 478table(m$Testing_6_CTA)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 567 644 279 81 32 12 17 4 2 4 28 12 1 3 1 3
## 18 26 88
## 2 1 8174table(m$Vaccine_7_CTA)##
## 0 1 2 3 4 5 6 7 8 9 15 88
## 429 272 134 26 11 10 10 5 2 1 2 8965
```r
p <- read_csv("DOH1.csv")## Rows: 9867 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Post_oldid, date, state
## dbl (29): Real_ID, COVID_Post, RiskFactor_2_CTA, SocialDisparities_3_CTA, De...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.p## # A tibble: 9,867 × 32
## Post_oldid Real_ID date COVID_Post RiskFactor_2_CTA SocialDisparities_3_…
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1 2069 9/22/20 1 88 88
## 2 2 2070 8/27/20 1 88 88
## 3 3 2071 12/29/20 1 88 88
## 4 4 2072 9/29/20 1 88 88
## 5 5 2073 10/6/20 1 88 88
## 6 6 2074 12/31/20 1 88 88
## 7 7 403 4/2/20 1 88 88
## 8 8 5425 12/1/20 1 88 88
## 9 9 5842 12/20/20 1 88 88
## 10 10 2075 3/5/20 1 88 88
## # … with 9,857 more rows, and 26 more variables: Debunk_4_CTA <dbl>,
## # UncertaintyReduction_5_CTA <dbl>, Testing_6_CTA <dbl>, Vaccine_7_CTA <dbl>,
## # Mental_8_YesNo <dbl>, Emotion_9_Number <dbl>, Praise_10_Number <dbl>,
## # Pop_11_Number <dbl>, Language_12_Number <dbl>, Action_13_CTA <dbl>,
## # Positive_14a_Number <dbl>, Negative_14b_Number <dbl>,
## # Individual_14c_Number <dbl>, Collective_14d_Number <dbl>,
## # `State_ recode` <dbl>, Gov_poli <dbl>, Trifecta <dbl>, …colnames(p)## [1] "Post_oldid" "Real_ID"
## [3] "date" "COVID_Post"
## [5] "RiskFactor_2_CTA" "SocialDisparities_3_CTA"
## [7] "Debunk_4_CTA" "UncertaintyReduction_5_CTA"
## [9] "Testing_6_CTA" "Vaccine_7_CTA"
## [11] "Mental_8_YesNo" "Emotion_9_Number"
## [13] "Praise_10_Number" "Pop_11_Number"
## [15] "Language_12_Number" "Action_13_CTA"
## [17] "Positive_14a_Number" "Negative_14b_Number"
## [19] "Individual_14c_Number" "Collective_14d_Number"
## [21] "State_ recode" "Gov_poli"
## [23] "Trifecta" "White_\bpercent"
## [25] "minority_percent" "Poverty_percent"
## [27] "ProspertiyRanking" "year"
## [29] "month" "day"
## [31] "state" "cases"summary(p)## Post_oldid Real_ID date COVID_Post
## Length:9867 Min. : 1 Length:9867 Min. :1
## Class :character 1st Qu.:2468 Class :character 1st Qu.:1
## Mode :character Median :4935 Mode :character Median :1
## Mean :4935 Mean :1
## 3rd Qu.:7402 3rd Qu.:1
## Max. :9868 Max. :1
##
## RiskFactor_2_CTA SocialDisparities_3_CTA Debunk_4_CTA
## Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:88.00 1st Qu.:88.00 1st Qu.:88.00
## Median :88.00 Median :88.00 Median :88.00
## Mean :76.49 Mean :86.46 Mean :84.21
## 3rd Qu.:88.00 3rd Qu.:88.00 3rd Qu.:88.00
## Max. :88.00 Max. :88.00 Max. :88.00
##
## UncertaintyReduction_5_CTA Testing_6_CTA Vaccine_7_CTA Mental_8_YesNo
## Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. :0.00000
## 1st Qu.: 1.000 1st Qu.:88.00 1st Qu.:88.00 1st Qu.:0.00000
## Median : 1.000 Median :88.00 Median :88.00 Median :0.00000
## Mean : 6.111 Mean :73.14 Mean :80.04 Mean :0.01824
## 3rd Qu.: 3.000 3rd Qu.:88.00 3rd Qu.:88.00 3rd Qu.:0.00000
## Max. :88.000 Max. :88.00 Max. :88.00 Max. :1.00000
##
## Emotion_9_Number Praise_10_Number Pop_11_Number Language_12_Number
## Min. :0.00000 Min. :0.00000 Min. : 0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0.00000 Median : 0.0000 Median :0.0000
## Mean :0.02017 Mean :0.04267 Mean : 0.1493 Mean :0.0824
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.:0.0000
## Max. :6.00000 Max. :8.00000 Max. :13.0000 Max. :6.0000
##
## Action_13_CTA Positive_14a_Number Negative_14b_Number Individual_14c_Number
## Min. : 0.000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.: 1.000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median : 1.000 Median :0.0000 Median :0.00000 Median :0.0000
## Mean : 2.493 Mean :0.6292 Mean :0.02017 Mean :0.1806
## 3rd Qu.: 2.000 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :88.000 Max. :8.0000 Max. :6.00000 Max. :7.0000
##
## Collective_14d_Number State_ recode Gov_poli Trifecta
## Min. :0.0000 Min. : 1.00 Min. :1.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.: 5.00 1st Qu.:1.000 1st Qu.:1.000
## Median :0.0000 Median :13.00 Median :1.000 Median :2.000
## Mean :0.4692 Mean :20.78 Mean :1.486 Mean :1.677
## 3rd Qu.:1.0000 3rd Qu.:34.00 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :8.0000 Max. :51.00 Max. :2.000 Max. :3.000
##
## White_\bpercent minority_percent Poverty_percent ProspertiyRanking
## Min. :21.70 Min. : 7.40 Min. : 7.50 Min. : 2.00
## 1st Qu.:53.20 1st Qu.:24.30 1st Qu.:10.40 1st Qu.:11.00
## Median :65.30 Median :34.70 Median :12.00 Median :28.00
## Mean :62.74 Mean :37.24 Mean :12.62 Mean :27.73
## 3rd Qu.:75.70 3rd Qu.:46.80 3rd Qu.:14.10 3rd Qu.:41.00
## Max. :92.60 Max. :78.30 Max. :18.80 Max. :51.00
##
## year month day state
## Min. :2020 Min. : 1.000 Min. : 1.00 Length:9867
## 1st Qu.:2020 1st Qu.: 5.000 1st Qu.: 9.00 Class :character
## Median :2020 Median : 9.000 Median :17.00 Mode :character
## Mean :2020 Mean : 8.152 Mean :16.32
## 3rd Qu.:2020 3rd Qu.:12.000 3rd Qu.:24.00
## Max. :2020 Max. :12.000 Max. :31.00
##
## cases
## Min. : 0
## 1st Qu.: 121
## Median : 583
## Mean : 2011
## 3rd Qu.: 2208
## Max. :64986
## NA's :422p1 <- p %>%
mutate(RiskFactor_2_CTA = recode(RiskFactor_2_CTA, '88' = 0,
'0'=1, '1'=1, '2'=1, '3'=1, '4'=1, '5'=1,
'6'=1, '7'=1, '8'=1, '9'=1, '10'=1,
'11'=1, '12'=1, '13'=1, '14'= 1, '15' =1,
'16' =1, '18' =1, '20'=1, '25'=1, '26'=1)) %>%
mutate(SocialDisparities_3_CTA = recode(SocialDisparities_3_CTA, '88' = 0,
'0'=1, '1'=1, '2'=1, '3'=1, '4'=1, '5'=1,
'6'=1, '7'=1, '8'=1, '9'=1, '10'=1,
'11'=1, '12'=1, '13'=1, '14'= 1, '15' =1,
'26'=1)) %>%
mutate(Debunk_4_CTA = recode(Debunk_4_CTA,'88' = 0,
'0'=1, '1'= 1, '2'=1, '3'=1, '4'=1, '5'=1,
'6'=1, '7'=1, '8'= 1, '9'= 1, '10'=1,
'11'=1, '15' =1)) %>%
mutate(UncertaintyReduction_5_CTA =recode(UncertaintyReduction_5_CTA, '88' = 0,
'0'=1, '1'=1, '2'=1, '3'=1, '4'=1, '5'=1,
'6'=1, '7'=1, '8'= 1, '9'=1, '10'=1,
'11'=1, '12'=1, '13'=1, '14'= 1, '15' =1,
'16' =1, '17'= 1, '18' =1, '25'=1, '26'=1)) %>%
mutate(Testing_6_CTA = recode(Testing_6_CTA, '88' = 0,
'0'=1, '1'=1, '2'=1, '3'=1, '4'=1, '5'=1,
'6'=1, '7'=1, '8'=1, '9'=1, '10'=1,
'11'=1, '12'=1, '13'=1, '14'= 1, '15' =1,
'18' =1, '25'=1, '26'=1)) %>%
mutate( Vaccine_7_CTA = recode(Vaccine_7_CTA, '88' = 0,
'0'=1, '1'=1, '2'=1, '3'=1, '4'=1, '5'=1,
'6'=1, '7'=1, '8'=1, '9'=1, '15' =1)) %>%
mutate( Action_13_CTA = recode(Action_13_CTA, '88' = 0,
'0'=1, '1'=1, '2'=1, '3'=1, '4'=1, '5'=1,
'6'=1, '7'=1, '8'=1, '9'=1, '15' =1, '16' =1, '18' =1,
'25'=1, '26'=1))
p1 ## # A tibble: 9,867 × 32
## Post_oldid Real_ID date COVID_Post RiskFactor_2_CTA SocialDisparities_3_…
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1 2069 9/22/20 1 0 0
## 2 2 2070 8/27/20 1 0 0
## 3 3 2071 12/29/20 1 0 0
## 4 4 2072 9/29/20 1 0 0
## 5 5 2073 10/6/20 1 0 0
## 6 6 2074 12/31/20 1 0 0
## 7 7 403 4/2/20 1 0 0
## 8 8 5425 12/1/20 1 0 0
## 9 9 5842 12/20/20 1 0 0
## 10 10 2075 3/5/20 1 0 0
## # … with 9,857 more rows, and 26 more variables: Debunk_4_CTA <dbl>,
## # UncertaintyReduction_5_CTA <dbl>, Testing_6_CTA <dbl>, Vaccine_7_CTA <dbl>,
## # Mental_8_YesNo <dbl>, Emotion_9_Number <dbl>, Praise_10_Number <dbl>,
## # Pop_11_Number <dbl>, Language_12_Number <dbl>, Action_13_CTA <dbl>,
## # Positive_14a_Number <dbl>, Negative_14b_Number <dbl>,
## # Individual_14c_Number <dbl>, Collective_14d_Number <dbl>,
## # `State_ recode` <dbl>, Gov_poli <dbl>, Trifecta <dbl>, …table(p1$Action_13_CTA)##
## 0 1 10 11 12 13 14 17
## 65 9705 62 23 3 4 4 1table(p1$Positive_14a_Number)##
## 0 1 2 3 4 5 6 7 8
## 5875 2563 949 289 124 35 20 7 5table(p1$Negative_14b_Number)##
## 0 1 2 3 5 6
## 9728 99 27 10 2 1table(p1$Individual_14c_Number)##
## 0 1 2 3 4 5 6 7
## 8523 1078 171 35 49 7 2 2table(p1$Collective_14d_Number)##
## 0 1 2 3 4 5 6 7 8
## 6635 2315 622 186 64 24 13 5 3p2 <- p1 %>%
select(-"Mental_8_YesNo",
-"Emotion_9_Number",
- "Praise_10_Number",
-"Pop_11_Number",
-"Language_12_Number") %>%
mutate(efficacy_total = Individual_14c_Number + Collective_14d_Number) %>%
mutate(efficacy_total1 = Negative_14b_Number + Positive_14a_Number) %>%
mutate(Gov_poli = factor(Gov_poli, levels = c(1,2), labels = c("Republican", "Democratic"))) %>%
mutate(Trifecta = factor(Trifecta, levels = c(1,2,3), labels = c("Republican", "Democratic", "Mixture")))
p2## # A tibble: 9,867 × 29
## Post_oldid Real_ID date COVID_Post RiskFactor_2_CTA SocialDisparities_3_…
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1 2069 9/22/20 1 0 0
## 2 2 2070 8/27/20 1 0 0
## 3 3 2071 12/29/20 1 0 0
## 4 4 2072 9/29/20 1 0 0
## 5 5 2073 10/6/20 1 0 0
## 6 6 2074 12/31/20 1 0 0
## 7 7 403 4/2/20 1 0 0
## 8 8 5425 12/1/20 1 0 0
## 9 9 5842 12/20/20 1 0 0
## 10 10 2075 3/5/20 1 0 0
## # … with 9,857 more rows, and 23 more variables: Debunk_4_CTA <dbl>,
## # UncertaintyReduction_5_CTA <dbl>, Testing_6_CTA <dbl>, Vaccine_7_CTA <dbl>,
## # Action_13_CTA <dbl>, Positive_14a_Number <dbl>, Negative_14b_Number <dbl>,
## # Individual_14c_Number <dbl>, Collective_14d_Number <dbl>,
## # `State_ recode` <dbl>, Gov_poli <fct>, Trifecta <fct>,
## # `White_\bpercent` <dbl>, minority_percent <dbl>, Poverty_percent <dbl>,
## # ProspertiyRanking <dbl>, year <dbl>, month <dbl>, day <dbl>, state <chr>, …levels(p2$Gov_poli)## [1] "Republican" "Democratic"ggplot(p2, aes(Action_13_CTA , fill = RiskFactor_2_CTA)) +
geom_histogram() +
scale_x_log10() +
facet_grid(RiskFactor_2_CTA ~ ., margins=TRUE, scales="free_y")## Warning: Transformation introduced infinite values in continuous x-axis## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 130 rows containing non-finite values (`stat_bin()`).
ggplot(p2, aes(Positive_14a_Number , fill = SocialDisparities_3_CTA)) +
geom_histogram() +
scale_x_log10() +
facet_grid(SocialDisparities_3_CTA ~ ., margins=TRUE, scales="free_y")## Warning: Transformation introduced infinite values in continuous x-axis## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 11750 rows containing non-finite values (`stat_bin()`).
ggplot(p2, aes(Negative_14b_Number, fill = RiskFactor_2_CTA)) +
geom_histogram() +
scale_x_log10() +
facet_grid(RiskFactor_2_CTA ~ ., margins=TRUE, scales="free_y")## Warning: Transformation introduced infinite values in continuous x-axis## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 19456 rows containing non-finite values (`stat_bin()`).
ggplot(p2, aes(Individual_14c_Number, fill = RiskFactor_2_CTA)) +
geom_histogram() +
scale_x_log10() +
facet_grid(RiskFactor_2_CTA ~ ., margins=TRUE, scales="free_y")## Warning: Transformation introduced infinite values in continuous x-axis## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 17046 rows containing non-finite values (`stat_bin()`).
library("dplyr")
library("ggpubr")
ggdensity(p2$Action_13_CTA,
main = "Density plot of Action_13_CTA",
xlab = "Individual_Action_13_CTA")
library("dplyr")
library("ggpubr")
ggdensity(p2$Positive_14a_Number,
main = "Density plot of Positive_14a_Number",
xlab = "Individual_Positive_14a_Number")
library("dplyr")
library("ggpubr")
ggdensity(p2$Negative_14b_Number,
main = "Negative_14b_Number",
xlab = "Negative_14b_Number")
library("dplyr")
library("ggpubr")
ggdensity(p2$Individual_14c_Number,
main = "Density plot of Individual_14c_Number",
xlab = "Individual_14c_Number")
library("dplyr")
library("ggpubr")
ggdensity(p2$Collective_14d_Number,
main = "Density plot of collective efficacy",
xlab = "Collective efficacy")
library("dplyr")
library("ggpubr")
ggdensity(p2$minority_percent,
main = "Density plot of minority_percent",
xlab = "minority_percent")
minority_percent Poverty_percent
library("dplyr")
library("ggpubr")
ggdensity(p2$Poverty_percent,
main = "Density plot of Poverty_percent",
xlab = "Poverty_percent")
summary(p1)## Post_oldid Real_ID date COVID_Post
## Length:9867 Min. : 1 Length:9867 Min. :1
## Class :character 1st Qu.:2468 Class :character 1st Qu.:1
## Mode :character Median :4935 Mode :character Median :1
## Mean :4935 Mean :1
## 3rd Qu.:7402 3rd Qu.:1
## Max. :9868 Max. :1
##
## RiskFactor_2_CTA SocialDisparities_3_CTA Debunk_4_CTA
## Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000 Median :0.00000
## Mean :0.1361 Mean :0.01784 Mean :0.04409
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.00000 Max. :1.00000
##
## UncertaintyReduction_5_CTA Testing_6_CTA Vaccine_7_CTA
## Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :1.0000 Median :0.0000 Median :0.00000
## Mean :0.9516 Mean :0.1716 Mean :0.09142
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.00000
##
## Mental_8_YesNo Emotion_9_Number Praise_10_Number Pop_11_Number
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. : 0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.: 0.0000
## Median :0.00000 Median :0.00000 Median :0.00000 Median : 0.0000
## Mean :0.01824 Mean :0.02017 Mean :0.04267 Mean : 0.1493
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.: 0.0000
## Max. :1.00000 Max. :6.00000 Max. :8.00000 Max. :13.0000
##
## Language_12_Number Action_13_CTA Positive_14a_Number Negative_14b_Number
## Min. :0.0000 Min. : 0.000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.: 1.000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median : 1.000 Median :0.0000 Median :0.00000
## Mean :0.0824 Mean : 1.088 Mean :0.6292 Mean :0.02017
## 3rd Qu.:0.0000 3rd Qu.: 1.000 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :6.0000 Max. :17.000 Max. :8.0000 Max. :6.00000
##
## Individual_14c_Number Collective_14d_Number State_ recode Gov_poli
## Min. :0.0000 Min. :0.0000 Min. : 1.00 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 5.00 1st Qu.:1.000
## Median :0.0000 Median :0.0000 Median :13.00 Median :1.000
## Mean :0.1806 Mean :0.4692 Mean :20.78 Mean :1.486
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:34.00 3rd Qu.:2.000
## Max. :7.0000 Max. :8.0000 Max. :51.00 Max. :2.000
##
## Trifecta White_\bpercent minority_percent Poverty_percent
## Min. :1.000 Min. :21.70 Min. : 7.40 Min. : 7.50
## 1st Qu.:1.000 1st Qu.:53.20 1st Qu.:24.30 1st Qu.:10.40
## Median :2.000 Median :65.30 Median :34.70 Median :12.00
## Mean :1.677 Mean :62.74 Mean :37.24 Mean :12.62
## 3rd Qu.:2.000 3rd Qu.:75.70 3rd Qu.:46.80 3rd Qu.:14.10
## Max. :3.000 Max. :92.60 Max. :78.30 Max. :18.80
##
## ProspertiyRanking year month day
## Min. : 2.00 Min. :2020 Min. : 1.000 Min. : 1.00
## 1st Qu.:11.00 1st Qu.:2020 1st Qu.: 5.000 1st Qu.: 9.00
## Median :28.00 Median :2020 Median : 9.000 Median :17.00
## Mean :27.73 Mean :2020 Mean : 8.152 Mean :16.32
## 3rd Qu.:41.00 3rd Qu.:2020 3rd Qu.:12.000 3rd Qu.:24.00
## Max. :51.00 Max. :2020 Max. :12.000 Max. :31.00
##
## state cases
## Length:9867 Min. : 0
## Class :character 1st Qu.: 121
## Mode :character Median : 583
## Mean : 2011
## 3rd Qu.: 2208
## Max. :64986
## NA's :422z-test efficacy positive - negative
#https://rpubs.com/nguyenminhsang/two-sample_z-test
# Set difference to be tested
d0<-0
d0## [1] 0# Set standard deviation of sample with status 0
sigma0<-sd(p1$Positive_14a_Number)
sigma0## [1] 0.9706897# Set standard deviation of sample with status 1
sigma1<-sd(p1$Negative_14b_Number)
sigma1## [1] 0.1959933#Calculate the two means
mean_positive <- mean(p1$Positive_14a_Number)
mean_positive## [1] 0.6291679 mean_negative <- mean(p1$Negative_14b_Number)
mean_negative## [1] 0.02016824 #Calculate the two lengths
n_positive<-length(p1$Positive_14a_Number)
n_positive## [1] 9867n_negative<-length(p1$Negative_14b_Number)
n_negative## [1] 9867# Calculate test statistic and two-sided p-value
z<-((mean_positive- mean_negative)-d0)/
sqrt(sigma0^2/n_positive+
sigma1^2/n_negative)
z## [1] 61.08748p_value=2*pnorm(-abs(z))
p_value## [1] 0###RQ1: To what extent do state health departments use self-efficacy statements (Q14 a vs Q14b AND Q14c vs. Q14d) in their Facebook posts between Jan-Dec, 2020?
z-test efficacy positive - negative
# Set difference to be tested
d1_0<-0
d1_0## [1] 0# Set standard deviation of sample with status 0
sigma1_0<-sd(p1$Individual_14c_Number)
sigma1_0## [1] 0.5406792# Set standard deviation of sample with status 1
sigma1_1<-sd(p1$Collective_14d_Number)
sigma1_1## [1] 0.8322395#Calculate the two means
mean_individual <- mean(p1$Individual_14c_Number)
mean_individual ## [1] 0.180602 mean_collective <- mean(p1$Collective_14d_Number)
mean_collective## [1] 0.4692409 #Calculate the two lengths
n_individual <-length(p1$Individual_14c_Number)
n_individual ## [1] 9867n_collective<-length(p1$Collective_14d_Number)
n_collective## [1] 9867# Calculate test statistic and two-sided p-value
z1 <-((mean_individual-mean_collective)-d1_0)/
sqrt(sigma1_0^2/n_individual +
sigma1_1^2/n_collective)
z1## [1] -28.88942p_value1 =2*pnorm(-abs(z))
p_value1 ## [1] 0###RQ3: How do self-efficacy statements in posts differ between states governed by Democrat and Republican governors?
#fit0 <- t.test(
# formula = efficacy_total1 ~ Gov_poli,
# data = p2,
# paired = FALSE,
# var.equal = TRUE)
#(fit0)
#library(parameters)
#model_parameters(fit0)fit <- t.test(
formula = efficacy_total ~ Gov_poli,
data = p2,
paired = FALSE,
var.equal = FALSE
)
(fit)##
## Welch Two Sample t-test
##
## data: efficacy_total by Gov_poli
## t = 0.51893, df = 9828.2, p-value = 0.6038
## alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0
## 95 percent confidence interval:
## -0.02889909 0.04970910
## sample estimates:
## mean in group Republican mean in group Democratic
## 0.6549004 0.6444954library(parameters)
model_parameters(fit)## Welch Two Sample t-test
##
## Parameter | Group | Gov_poli = Republican | Gov_poli = Democratic | Difference | 95% CI | t(9828.17) | p
## ---------------------------------------------------------------------------------------------------------------------------
## efficacy_total | Gov_poli | 0.65 | 0.64 | 0.01 | [-0.03, 0.05] | 0.52 | 0.604
##
## Alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0fit1 <- t.test(
formula = Positive_14a_Number ~ Gov_poli,
data = p2,
paired = FALSE,
var.equal = FALSE
)
(fit1)##
## Welch Two Sample t-test
##
## data: Positive_14a_Number by Gov_poli
## t = 1.1311, df = 9846.1, p-value = 0.2581
## alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0
## 95 percent confidence interval:
## -0.01620549 0.06041895
## sample estimates:
## mean in group Republican mean in group Democratic
## 0.6399132 0.6178065library(parameters)
model_parameters(fit1)## Welch Two Sample t-test
##
## Parameter | Group | Gov_poli = Republican | Gov_poli = Democratic | Difference | 95% CI | t(9846.09) | p
## --------------------------------------------------------------------------------------------------------------------------------
## Positive_14a_Number | Gov_poli | 0.64 | 0.62 | 0.02 | [-0.02, 0.06] | 1.13 | 0.258
##
## Alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0fit2 <- t.test(
formula = Negative_14b_Number ~ Gov_poli,
data = p2,
paired = FALSE,
var.equal = FALSE
)
(fit2)##
## Welch Two Sample t-test
##
## data: Negative_14b_Number by Gov_poli
## t = -3.1853, df = 8559.1, p-value = 0.001451
## alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0
## 95 percent confidence interval:
## -0.020495816 -0.004879633
## sample estimates:
## mean in group Republican mean in group Democratic
## 0.01400118 0.02668891library(parameters)
model_parameters(fit2)## Welch Two Sample t-test
##
## Parameter | Group | Gov_poli = Republican | Gov_poli = Democratic | Difference | 95% CI | t(8559.06) | p
## ---------------------------------------------------------------------------------------------------------------------------------
## Negative_14b_Number | Gov_poli | 0.01 | 0.03 | -0.01 | [-0.02, 0.00] | -3.19 | 0.001
##
## Alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0fit3 <- t.test(
formula = Individual_14c_Number ~ Gov_poli,
data = p2,
paired = FALSE,
var.equal = FALSE
)
(fit3)##
## Welch Two Sample t-test
##
## data: Individual_14c_Number by Gov_poli
## t = 2.9207, df = 9850.9, p-value = 0.0035
## alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0
## 95 percent confidence interval:
## 0.01042905 0.05299690
## sample estimates:
## mean in group Republican mean in group Democratic
## 0.1960166 0.1643036library(parameters)
model_parameters(fit3)## Welch Two Sample t-test
##
## Parameter | Group | Gov_poli = Republican | Gov_poli = Democratic | Difference | 95% CI | t(9850.88) | p
## ---------------------------------------------------------------------------------------------------------------------------------
## Individual_14c_Number | Gov_poli | 0.20 | 0.16 | 0.03 | [0.01, 0.05] | 2.92 | 0.004
##
## Alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0fit4 <- t.test(
formula = Collective_14d_Number ~ Gov_poli,
data = p2,
paired = FALSE,
var.equal = FALSE
)
(fit1)##
## Welch Two Sample t-test
##
## data: Positive_14a_Number by Gov_poli
## t = 1.1311, df = 9846.1, p-value = 0.2581
## alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0
## 95 percent confidence interval:
## -0.01620549 0.06041895
## sample estimates:
## mean in group Republican mean in group Democratic
## 0.6399132 0.6178065library(parameters)
model_parameters(fit4)## Welch Two Sample t-test
##
## Parameter | Group | Gov_poli = Republican | Gov_poli = Democratic | Difference | 95% CI | t(9813.00) | p
## ----------------------------------------------------------------------------------------------------------------------------------
## Collective_14d_Number | Gov_poli | 0.46 | 0.48 | -0.02 | [-0.05, 0.01] | -1.27 | 0.204
##
## Alternative hypothesis: true difference in means between group Republican and group Democratic is not equal to 0library(effectsize)
cohens_d(fit)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## --------------------
## 0.01 | [-0.03, 0.05]cohens_d(fit1)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## --------------------
## 0.02 | [-0.02, 0.06]cohens_d(fit2)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## ----------------------
## -0.07 | [-0.11, -0.03]cohens_d(fit3)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## -------------------
## 0.06 | [0.02, 0.10]cohens_d(fit4)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## ---------------------
## -0.03 | [-0.07, 0.01]###RQ4: How do self-efficacy statements in posts differ between states governed by Democrat and Republican states?
colnames(p2)## [1] "Post_oldid" "Real_ID"
## [3] "date" "COVID_Post"
## [5] "RiskFactor_2_CTA" "SocialDisparities_3_CTA"
## [7] "Debunk_4_CTA" "UncertaintyReduction_5_CTA"
## [9] "Testing_6_CTA" "Vaccine_7_CTA"
## [11] "Action_13_CTA" "Positive_14a_Number"
## [13] "Negative_14b_Number" "Individual_14c_Number"
## [15] "Collective_14d_Number" "State_ recode"
## [17] "Gov_poli" "Trifecta"
## [19] "White_\bpercent" "minority_percent"
## [21] "Poverty_percent" "ProspertiyRanking"
## [23] "year" "month"
## [25] "day" "state"
## [27] "cases" "efficacy_total"
## [29] "efficacy_total1"levels(p2$Trifecta)## [1] "Republican" "Democratic" "Mixture"fit_0 <- aov(formula = efficacy_total ~ Trifecta, data = p2)
summary(fit_0)## Df Sum Sq Mean Sq F value Pr(>F)
## Trifecta 2 8 3.892 3.931 0.0196 *
## Residuals 9864 9765 0.990
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_parameters(fit_0)## Parameter | Sum_Squares | df | Mean_Square | F | p
## -----------------------------------------------------------
## Trifecta | 7.78 | 2 | 3.89 | 3.93 | 0.020
## Residuals | 9765.42 | 9864 | 0.99 | |
##
## Anova Table (Type 1 tests)library(ggeffects)
gge_0 <- ggeffect(fit_0, "Trifecta")## Package `effects` is not available, but needed for `ggeffect()`. Either install package `effects`, or use `ggpredict()`. Calling `ggpredict()` now.FALSEgge_0## # Predicted values of efficacy_total
##
## Trifecta | Predicted | 95% CI
## -------------------------------------
## Republican | 0.64 | [0.61, 0.66]
## Democratic | 0.64 | [0.61, 0.67]
## Mixture | 0.72 | [0.67, 0.77]plot(gge_0, connect.lines = TRUE)
fit_1 <- aov(formula = Positive_14a_Number ~ Trifecta, data = p2)
summary(fit_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Trifecta 2 6 2.9686 3.152 0.0428 *
## Residuals 9864 9290 0.9418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_parameters(fit_1)## Parameter | Sum_Squares | df | Mean_Square | F | p
## -----------------------------------------------------------
## Trifecta | 5.94 | 2 | 2.97 | 3.15 | 0.043
## Residuals | 9290.19 | 9864 | 0.94 | |
##
## Anova Table (Type 1 tests)gge_1 <- ggeffect(fit_1, "Trifecta")## Package `effects` is not available, but needed for `ggeffect()`. Either install package `effects`, or use `ggpredict()`. Calling `ggpredict()` now.FALSEgge_1## # Predicted values of Positive_14a_Number
##
## Trifecta | Predicted | 95% CI
## -------------------------------------
## Republican | 0.62 | [0.60, 0.65]
## Democratic | 0.61 | [0.58, 0.64]
## Mixture | 0.69 | [0.64, 0.74]plot(gge_1, connect.lines = TRUE)
fit_2 <- aov(formula = Negative_14b_Number ~ Trifecta, data = p2)
summary(fit_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Trifecta 2 0.9 0.4377 11.42 1.11e-05 ***
## Residuals 9864 378.1 0.0383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_parameters(fit_2)## Parameter | Sum_Squares | df | Mean_Square | F | p
## -------------------------------------------------------------
## Trifecta | 0.88 | 2 | 0.44 | 11.42 | < .001
## Residuals | 378.11 | 9864 | 0.04 | |
##
## Anova Table (Type 1 tests)gge_2 <- ggeffect(fit_2, "Trifecta")## Package `effects` is not available, but needed for `ggeffect()`. Either install package `effects`, or use `ggpredict()`. Calling `ggpredict()` now.FALSEgge_2## # Predicted values of Negative_14b_Number
##
## Trifecta | Predicted | 95% CI
## -------------------------------------
## Republican | 0.01 | [0.00, 0.02]
## Democratic | 0.03 | [0.02, 0.04]
## Mixture | 0.03 | [0.02, 0.04]plot(gge_2, connect.lines = TRUE)
fit_3 <- aov(formula = Individual_14c_Number ~ Trifecta, data = p2)
summary(fit_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Trifecta 2 4.2 2.088 7.153 0.000787 ***
## Residuals 9864 2880.0 0.292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_parameters(fit_3)## Parameter | Sum_Squares | df | Mean_Square | F | p
## ------------------------------------------------------------
## Trifecta | 4.18 | 2 | 2.09 | 7.15 | < .001
## Residuals | 2879.99 | 9864 | 0.29 | |
##
## Anova Table (Type 1 tests)gge_3 <- ggeffect(fit_3, "Trifecta")## Package `effects` is not available, but needed for `ggeffect()`. Either install package `effects`, or use `ggpredict()`. Calling `ggpredict()` now.FALSEgge_3## # Predicted values of Individual_14c_Number
##
## Trifecta | Predicted | 95% CI
## -------------------------------------
## Republican | 0.20 | [0.19, 0.22]
## Democratic | 0.16 | [0.14, 0.18]
## Mixture | 0.16 | [0.14, 0.19]plot(gge_3, connect.lines = TRUE)
fit_4 <- aov(formula = Collective_14d_Number ~ Trifecta, data = p2)
summary(fit_4)## Df Sum Sq Mean Sq F value Pr(>F)
## Trifecta 2 17 8.389 12.14 5.43e-06 ***
## Residuals 9864 6817 0.691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model_parameters(fit_4)## Parameter | Sum_Squares | df | Mean_Square | F | p
## -------------------------------------------------------------
## Trifecta | 16.78 | 2 | 8.39 | 12.14 | < .001
## Residuals | 6816.64 | 9864 | 0.69 | |
##
## Anova Table (Type 1 tests)gge_4 <- ggeffect(fit_4, "Trifecta")## Package `effects` is not available, but needed for `ggeffect()`. Either install package `effects`, or use `ggpredict()`. Calling `ggpredict()` now.FALSEgge_4## # Predicted values of Collective_14d_Number
##
## Trifecta | Predicted | 95% CI
## -------------------------------------
## Republican | 0.43 | [0.41, 0.46]
## Democratic | 0.48 | [0.46, 0.51]
## Mixture | 0.55 | [0.51, 0.60]plot(gge_4, connect.lines = TRUE)
RQ5: How does the socio-economic status affect use of individual/collective self-efficacy in health communication messages?
p2 ## # A tibble: 9,867 × 29
## Post_oldid Real_ID date COVID_Post RiskFactor_2_CTA SocialDisparities_3_…
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1 2069 9/22/20 1 0 0
## 2 2 2070 8/27/20 1 0 0
## 3 3 2071 12/29/20 1 0 0
## 4 4 2072 9/29/20 1 0 0
## 5 5 2073 10/6/20 1 0 0
## 6 6 2074 12/31/20 1 0 0
## 7 7 403 4/2/20 1 0 0
## 8 8 5425 12/1/20 1 0 0
## 9 9 5842 12/20/20 1 0 0
## 10 10 2075 3/5/20 1 0 0
## # … with 9,857 more rows, and 23 more variables: Debunk_4_CTA <dbl>,
## # UncertaintyReduction_5_CTA <dbl>, Testing_6_CTA <dbl>, Vaccine_7_CTA <dbl>,
## # Action_13_CTA <dbl>, Positive_14a_Number <dbl>, Negative_14b_Number <dbl>,
## # Individual_14c_Number <dbl>, Collective_14d_Number <dbl>,
## # `State_ recode` <dbl>, Gov_poli <fct>, Trifecta <fct>,
## # `White_\bpercent` <dbl>, minority_percent <dbl>, Poverty_percent <dbl>,
## # ProspertiyRanking <dbl>, year <dbl>, month <dbl>, day <dbl>, state <chr>, …colnames(p2)## [1] "Post_oldid" "Real_ID"
## [3] "date" "COVID_Post"
## [5] "RiskFactor_2_CTA" "SocialDisparities_3_CTA"
## [7] "Debunk_4_CTA" "UncertaintyReduction_5_CTA"
## [9] "Testing_6_CTA" "Vaccine_7_CTA"
## [11] "Action_13_CTA" "Positive_14a_Number"
## [13] "Negative_14b_Number" "Individual_14c_Number"
## [15] "Collective_14d_Number" "State_ recode"
## [17] "Gov_poli" "Trifecta"
## [19] "White_\bpercent" "minority_percent"
## [21] "Poverty_percent" "ProspertiyRanking"
## [23] "year" "month"
## [25] "day" "state"
## [27] "cases" "efficacy_total"
## [29] "efficacy_total1"p3 <-p2 %>%
rename( White_percent = "White_\bpercent") %>%
select(minority_percent, Poverty_percent, Positive_14a_Number, Negative_14b_Number, Individual_14c_Number, Collective_14d_Number, efficacy_total)
p3## # A tibble: 9,867 × 7
## minority_percent Poverty_percent Positive_14a_Number Negative_14b_Number
## <dbl> <dbl> <dbl> <dbl>
## 1 63.5 11.8 0 0
## 2 63.5 11.8 0 0
## 3 63.5 11.8 0 0
## 4 63.5 11.8 0 0
## 5 63.5 11.8 0 0
## 6 63.5 11.8 0 0
## 7 39.8 10.2 0 0
## 8 45.4 9.1 0 0
## 9 28.6 11.6 0 0
## 10 63.5 11.8 1 0
## # … with 9,857 more rows, and 3 more variables: Individual_14c_Number <dbl>,
## # Collective_14d_Number <dbl>, efficacy_total <dbl>library(correlation)
correlation(p3)## # Correlation Matrix (pearson-method)
##
## Parameter1 | Parameter2 | r | 95% CI | t(9865) | p
## -----------------------------------------------------------------------------------------------
## minority_percent | Poverty_percent | 0.32 | [ 0.30, 0.34] | 33.62 | < .001***
## minority_percent | Positive_14a_Number | 0.08 | [ 0.06, 0.10] | 8.20 | < .001***
## minority_percent | Negative_14b_Number | 0.02 | [ 0.00, 0.04] | 2.33 | 0.100
## minority_percent | Individual_14c_Number | -0.02 | [-0.04, 0.00] | -1.94 | 0.157
## minority_percent | Collective_14d_Number | 0.12 | [ 0.10, 0.13] | 11.52 | < .001***
## minority_percent | efficacy_total | 0.09 | [ 0.07, 0.11] | 8.54 | < .001***
## Poverty_percent | Positive_14a_Number | -0.04 | [-0.05, -0.02] | -3.49 | 0.004**
## Poverty_percent | Negative_14b_Number | -0.02 | [-0.04, 0.00] | -1.90 | 0.157
## Poverty_percent | Individual_14c_Number | -0.02 | [-0.04, 0.00] | -2.20 | 0.113
## Poverty_percent | Collective_14d_Number | -0.03 | [-0.05, -0.01] | -3.18 | 0.010*
## Poverty_percent | efficacy_total | -0.04 | [-0.06, -0.02] | -3.85 | 0.001**
## Positive_14a_Number | Negative_14b_Number | 0.02 | [ 0.01, 0.04] | 2.48 | 0.080
## Positive_14a_Number | Individual_14c_Number | 0.52 | [ 0.50, 0.53] | 60.01 | < .001***
## Positive_14a_Number | Collective_14d_Number | 0.83 | [ 0.83, 0.84] | 149.60 | < .001***
## Positive_14a_Number | efficacy_total | 0.98 | [ 0.98, 0.98] | 460.89 | < .001***
## Negative_14b_Number | Individual_14c_Number | 0.21 | [ 0.19, 0.23] | 21.49 | < .001***
## Negative_14b_Number | Collective_14d_Number | 0.13 | [ 0.11, 0.15] | 12.73 | < .001***
## Negative_14b_Number | efficacy_total | 0.22 | [ 0.20, 0.24] | 22.53 | < .001***
## Individual_14c_Number | Collective_14d_Number | 6.26e-03 | [-0.01, 0.03] | 0.62 | 0.534
## Individual_14c_Number | efficacy_total | 0.55 | [ 0.53, 0.56] | 65.15 | < .001***
## Collective_14d_Number | efficacy_total | 0.84 | [ 0.83, 0.85] | 153.51 | < .001***
##
## p-value adjustment method: Holm (1979)
## Observations: 9867rmat <- correlation(p3)
plot(summary(rmat))
###RQ6: To what extent do state health departments use call to action in relations to risk (Q2, 3,4,5,6,7)?
p2## # A tibble: 9,867 × 29
## Post_oldid Real_ID date COVID_Post RiskFactor_2_CTA SocialDisparities_3_…
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1 2069 9/22/20 1 0 0
## 2 2 2070 8/27/20 1 0 0
## 3 3 2071 12/29/20 1 0 0
## 4 4 2072 9/29/20 1 0 0
## 5 5 2073 10/6/20 1 0 0
## 6 6 2074 12/31/20 1 0 0
## 7 7 403 4/2/20 1 0 0
## 8 8 5425 12/1/20 1 0 0
## 9 9 5842 12/20/20 1 0 0
## 10 10 2075 3/5/20 1 0 0
## # … with 9,857 more rows, and 23 more variables: Debunk_4_CTA <dbl>,
## # UncertaintyReduction_5_CTA <dbl>, Testing_6_CTA <dbl>, Vaccine_7_CTA <dbl>,
## # Action_13_CTA <dbl>, Positive_14a_Number <dbl>, Negative_14b_Number <dbl>,
## # Individual_14c_Number <dbl>, Collective_14d_Number <dbl>,
## # `State_ recode` <dbl>, Gov_poli <fct>, Trifecta <fct>,
## # `White_\bpercent` <dbl>, minority_percent <dbl>, Poverty_percent <dbl>,
## # ProspertiyRanking <dbl>, year <dbl>, month <dbl>, day <dbl>, state <chr>, …colnames(p2)## [1] "Post_oldid" "Real_ID"
## [3] "date" "COVID_Post"
## [5] "RiskFactor_2_CTA" "SocialDisparities_3_CTA"
## [7] "Debunk_4_CTA" "UncertaintyReduction_5_CTA"
## [9] "Testing_6_CTA" "Vaccine_7_CTA"
## [11] "Action_13_CTA" "Positive_14a_Number"
## [13] "Negative_14b_Number" "Individual_14c_Number"
## [15] "Collective_14d_Number" "State_ recode"
## [17] "Gov_poli" "Trifecta"
## [19] "White_\bpercent" "minority_percent"
## [21] "Poverty_percent" "ProspertiyRanking"
## [23] "year" "month"
## [25] "day" "state"
## [27] "cases" "efficacy_total"
## [29] "efficacy_total1"p2$RiskFactor_2_CTA## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0
## [38] 0 0 0 0 0 0 1 0 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0
## [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0
## [112] 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0
## [149] 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
## [186] 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [260] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [297] 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [334] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 0
## [371] 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1
## [408] 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
## [445] 0 0 0 0 1 0 0 1 0 1 1 1 1 1 1 0 1 0 1 0 1 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0
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## [9843] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0fita_0 <- t.test(
formula = Action_13_CTA ~ RiskFactor_2_CTA,
data = p2,
paired = FALSE,
var.equal = FALSE
)
fita_0##
## Welch Two Sample t-test
##
## data: Action_13_CTA by RiskFactor_2_CTA
## t = -4.451, df = 1455.6, p-value = 9.198e-06
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.2847528 -0.1105429
## sample estimates:
## mean in group 0 mean in group 1
## 1.061473 1.259121library(parameters)
model_parameters(fita_0)## Welch Two Sample t-test
##
## Parameter | Group | RiskFactor_2_CTA = 0 | RiskFactor_2_CTA = 1 | Difference | 95% CI | t(1455.63) | p
## ----------------------------------------------------------------------------------------------------------------------------------
## Action_13_CTA | RiskFactor_2_CTA | 1.06 | 1.26 | -0.20 | [-0.28, -0.11] | -4.45 | < .001
##
## Alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0fita_1 <- t.test(
formula = Action_13_CTA ~ SocialDisparities_3_CTA,
data = p2,
paired = FALSE,
var.equal = FALSE
)
fita_1##
## Welch Two Sample t-test
##
## data: Action_13_CTA by SocialDisparities_3_CTA
## t = 0.55721, df = 185.64, p-value = 0.5781
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.08162854 0.14588920
## sample estimates:
## mean in group 0 mean in group 1
## 1.088949 1.056818model_parameters(fita_1)## Welch Two Sample t-test
##
## Parameter | Group | SocialDisparities_3_CTA = 0 | SocialDisparities_3_CTA = 1 | Difference | 95% CI | t(185.64) | p
## ----------------------------------------------------------------------------------------------------------------------------------------------------
## Action_13_CTA | SocialDisparities_3_CTA | 1.09 | 1.06 | 0.03 | [-0.08, 0.15] | 0.56 | 0.578
##
## Alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0fita_2 <- t.test(
formula = Action_13_CTA ~ Debunk_4_CTA,
data = p2,
paired = FALSE,
var.equal = FALSE
)
fita_2##
## Welch Two Sample t-test
##
## data: Action_13_CTA by Debunk_4_CTA
## t = 2.9728, df = 610.73, p-value = 0.003067
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## 0.02566369 0.12557055
## sample estimates:
## mean in group 0 mean in group 1
## 1.091709 1.016092model_parameters(fita_2)## Welch Two Sample t-test
##
## Parameter | Group | Debunk_4_CTA = 0 | Debunk_4_CTA = 1 | Difference | 95% CI | t(610.73) | p
## ------------------------------------------------------------------------------------------------------------------
## Action_13_CTA | Debunk_4_CTA | 1.09 | 1.02 | 0.08 | [0.03, 0.13] | 2.97 | 0.003
##
## Alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0fita_3 <- t.test(
formula = Action_13_CTA ~ UncertaintyReduction_5_CTA,
data = p2,
paired = FALSE,
var.equal = FALSE
)
fita_3##
## Welch Two Sample t-test
##
## data: Action_13_CTA by UncertaintyReduction_5_CTA
## t = -5.5825, df = 572.93, p-value = 3.663e-08
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.2563227 -0.1228997
## sample estimates:
## mean in group 0 mean in group 1
## 0.9079498 1.0975610model_parameters(fita_3)## Welch Two Sample t-test
##
## Parameter | Group | UncertaintyReduction_5_CTA = 0 | UncertaintyReduction_5_CTA = 1 | Difference | 95% CI | t(572.93) | p
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------
## Action_13_CTA | UncertaintyReduction_5_CTA | 0.91 | 1.10 | -0.19 | [-0.26, -0.12] | -5.58 | < .001
##
## Alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0fita_5 <- t.test(
formula = Action_13_CTA ~ Testing_6_CTA,
data = p2,
paired = FALSE,
var.equal = FALSE
)
fita_5##
## Welch Two Sample t-test
##
## data: Action_13_CTA by Testing_6_CTA
## t = -6.0874, df = 1830.3, p-value = 1.395e-09
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## -0.3321985 -0.1703007
## sample estimates:
## mean in group 0 mean in group 1
## 1.045265 1.296515model_parameters(fita_5)## Welch Two Sample t-test
##
## Parameter | Group | Testing_6_CTA = 0 | Testing_6_CTA = 1 | Difference | 95% CI | t(1830.26) | p
## -------------------------------------------------------------------------------------------------------------------------
## Action_13_CTA | Testing_6_CTA | 1.05 | 1.30 | -0.25 | [-0.33, -0.17] | -6.09 | < .001
##
## Alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0fita_6 <- t.test(
formula = Action_13_CTA ~ Vaccine_7_CTA,
data = p2,
paired = FALSE,
var.equal = FALSE
)
fita_6##
## Welch Two Sample t-test
##
## data: Action_13_CTA by Vaccine_7_CTA
## t = 5.6039, df = 3032.7, p-value = 2.284e-08
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
## 0.05609508 0.11647576
## sample estimates:
## mean in group 0 mean in group 1
## 1.096263 1.009978model_parameters(fita_6)## Welch Two Sample t-test
##
## Parameter | Group | Vaccine_7_CTA = 0 | Vaccine_7_CTA = 1 | Difference | 95% CI | t(3032.69) | p
## -----------------------------------------------------------------------------------------------------------------------
## Action_13_CTA | Vaccine_7_CTA | 1.10 | 1.01 | 0.09 | [0.06, 0.12] | 5.60 | < .001
##
## Alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0library(effectsize)
cohens_d(fita_0)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## ----------------------
## -0.23 | [-0.34, -0.13]cohens_d(fita_1)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## --------------------
## 0.08 | [-0.21, 0.37]cohens_d(fita_2)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## -------------------
## 0.24 | [0.08, 0.40]cohens_d(fita_3)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## ----------------------
## -0.47 | [-0.63, -0.30]cohens_d(fita_5)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## ----------------------
## -0.28 | [-0.38, -0.19]cohens_d(fita_6)## Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...):
## Unable to retrieve data from htest object. Using t_to_d() approximation.## d | 95% CI
## -------------------
## 0.20 | [0.13, 0.27]RQ7: How does the socio-economic status affect use of call to action in health communication messages?
p4 <- p2 %>%
select(minority_percent, Poverty_percent, Action_13_CTA)
p4## # A tibble: 9,867 × 3
## minority_percent Poverty_percent Action_13_CTA
## <dbl> <dbl> <dbl>
## 1 63.5 11.8 1
## 2 63.5 11.8 1
## 3 63.5 11.8 1
## 4 63.5 11.8 1
## 5 63.5 11.8 1
## 6 63.5 11.8 1
## 7 39.8 10.2 1
## 8 45.4 9.1 1
## 9 28.6 11.6 1
## 10 63.5 11.8 1
## # … with 9,857 more rowscorrelation(p4)## # Correlation Matrix (pearson-method)
##
## Parameter1 | Parameter2 | r | 95% CI | t(9865) | p
## ------------------------------------------------------------------------------------
## minority_percent | Poverty_percent | 0.32 | [ 0.30, 0.34] | 33.62 | < .001***
## minority_percent | Action_13_CTA | 4.27e-03 | [-0.02, 0.02] | 0.42 | 0.672
## Poverty_percent | Action_13_CTA | -0.06 | [-0.08, -0.04] | -5.77 | < .001***
##
## p-value adjustment method: Holm (1979)
## Observations: 9867rmat1 <- correlation(p4)
plot(summary(rmat1))
fit_0a <- lm(Action_13_CTA ~ minority_percent, data = p4 )
fit_0a##
## Call:
## lm(formula = Action_13_CTA ~ minority_percent, data = p4)
##
## Coefficients:
## (Intercept) minority_percent
## 1.0791449 0.0002479summary(fit_0a)##
## Call:
## lm(formula = Action_13_CTA ~ minority_percent, data = p4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0949 -0.0907 -0.0877 -0.0846 15.9110
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0791449 0.0238420 45.262 <2e-16 ***
## minority_percent 0.0002479 0.0005847 0.424 0.672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9647 on 9865 degrees of freedom
## Multiple R-squared: 1.822e-05, Adjusted R-squared: -8.315e-05
## F-statistic: 0.1797 on 1 and 9865 DF, p-value: 0.6716model_parameters(fit_0a)## Parameter | Coefficient | SE | 95% CI | t(9865) | p
## ----------------------------------------------------------------------------
## (Intercept) | 1.08 | 0.02 | [ 1.03, 1.13] | 45.26 | < .001
## minority percent | 2.48e-04 | 5.85e-04 | [ 0.00, 0.00] | 0.42 | 0.672##
## Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
## using a Wald t-distribution approximation.library(ggeffects)
plot(ggpredict(fit_0a, terms = "minority_percent"))
fit_0b <- lm(Action_13_CTA ~ Poverty_percent, data = p4 )
fit_0b##
## Call:
## lm(formula = Action_13_CTA ~ Poverty_percent, data = p4)
##
## Coefficients:
## (Intercept) Poverty_percent
## 1.3636 -0.0218summary(fit_0b)##
## Call:
## lm(formula = Action_13_CTA ~ Poverty_percent, data = p4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1717 -0.1368 -0.0867 -0.0562 15.8588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.363552 0.048631 28.039 < 2e-16 ***
## Poverty_percent -0.021801 0.003775 -5.774 7.96e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9631 on 9865 degrees of freedom
## Multiple R-squared: 0.003369, Adjusted R-squared: 0.003268
## F-statistic: 33.34 on 1 and 9865 DF, p-value: 7.958e-09model_parameters(fit_0b)## Parameter | Coefficient | SE | 95% CI | t(9865) | p
## ----------------------------------------------------------------------------
## (Intercept) | 1.36 | 0.05 | [ 1.27, 1.46] | 28.04 | < .001
## Poverty percent | -0.02 | 3.78e-03 | [-0.03, -0.01] | -5.77 | < .001##
## Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
## using a Wald t-distribution approximation.library(ggeffects)
plot(ggpredict(fit_0b, terms = "Poverty_percent"))
library(performance)
compare_performance(fit_0a, fit_0b, metrics = c("R2", "R2_adj"))## # Comparison of Model Performance Indices
##
## Name | Model | R2 | R2 (adj.)
## ---------------------------------------
## fit_0a | lm | 1.822e-05 | -8.315e-05
## fit_0b | lm | 0.003 | 0.003Partial effects
fit_1a <- lm (Action_13_CTA ~ minority_percent + Poverty_percent, data = p4 )
fit_1a##
## Call:
## lm(formula = Action_13_CTA ~ minority_percent + Poverty_percent,
## data = p4)
##
## Coefficients:
## (Intercept) minority_percent Poverty_percent
## 1.347166 0.001481 -0.024871summary(fit_1a)##
## Call:
## lm(formula = Action_13_CTA ~ minority_percent + Poverty_percent,
## data = p4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1612 -0.1477 -0.1006 -0.0432 15.8476
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3471664 0.0490950 27.440 < 2e-16 ***
## minority_percent 0.0014808 0.0006161 2.403 0.0163 *
## Poverty_percent -0.0248714 0.0039849 -6.241 4.51e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9628 on 9864 degrees of freedom
## Multiple R-squared: 0.003952, Adjusted R-squared: 0.00375
## F-statistic: 19.57 on 2 and 9864 DF, p-value: 3.301e-09parameters(fit_1a)## Parameter | Coefficient | SE | 95% CI | t(9864) | p
## -----------------------------------------------------------------------------
## (Intercept) | 1.35 | 0.05 | [ 1.25, 1.44] | 27.44 | < .001
## minority percent | 1.48e-03 | 6.16e-04 | [ 0.00, 0.00] | 2.40 | 0.016
## Poverty percent | -0.02 | 3.98e-03 | [-0.03, -0.02] | -6.24 | < .001##
## Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
## using a Wald t-distribution approximation.performance(fit_1a)## # Indices of model performance
##
## AIC | BIC | R2 | R2 (adj.) | RMSE | Sigma
## ---------------------------------------------------------
## 27259.182 | 27287.969 | 0.004 | 0.004 | 0.963 | 0.963plot(ggpredict(fit_1a, terms = c("minority_percent", "Poverty_percent[10,12,15]")))
Simple effect
fit_1b <- lm (Action_13_CTA ~ minority_percent * Poverty_percent, data = p4 )
fit_1b##
## Call:
## lm(formula = Action_13_CTA ~ minority_percent * Poverty_percent,
## data = p4)
##
## Coefficients:
## (Intercept) minority_percent
## 0.772448 0.017797
## Poverty_percent minority_percent:Poverty_percent
## 0.018443 -0.001198summary(fit_1b)##
## Call:
## lm(formula = Action_13_CTA ~ minority_percent * Poverty_percent,
## data = p4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2223 -0.1466 -0.0889 -0.0511 15.8175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7724484 0.1156585 6.679 2.54e-11 ***
## minority_percent 0.0177970 0.0030370 5.860 4.77e-09 ***
## Poverty_percent 0.0184435 0.0088411 2.086 0.037 *
## minority_percent:Poverty_percent -0.0011983 0.0002184 -5.486 4.21e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9614 on 9863 degrees of freedom
## Multiple R-squared: 0.006982, Adjusted R-squared: 0.00668
## F-statistic: 23.12 on 3 and 9863 DF, p-value: 6.615e-15parameters(fit_1b)## Parameter | Coefficient | SE | 95% CI | t(9863) | p
## -----------------------------------------------------------------------------------------------
## (Intercept) | 0.77 | 0.12 | [ 0.55, 1.00] | 6.68 | < .001
## minority percent | 0.02 | 3.04e-03 | [ 0.01, 0.02] | 5.86 | < .001
## Poverty percent | 0.02 | 8.84e-03 | [ 0.00, 0.04] | 2.09 | 0.037
## minority percent * Poverty percent | -1.20e-03 | 2.18e-04 | [ 0.00, 0.00] | -5.49 | < .001##
## Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
## using a Wald t-distribution approximation.performance(fit_1b)## # Indices of model performance
##
## AIC | BIC | R2 | R2 (adj.) | RMSE | Sigma
## ---------------------------------------------------------
## 27231.116 | 27267.101 | 0.007 | 0.007 | 0.961 | 0.961plot(ggpredict(fit_1b, terms = c("minority_percent", "Poverty_percent[10,12,15]")))
no need centering because 0 % is meaningful
RQ8: How does efficacy statements predict call to actions?
p5 <- p2 %>%
select(Action_13_CTA, Positive_14a_Number, Negative_14b_Number, Individual_14c_Number, Collective_14d_Number)
p5## # A tibble: 9,867 × 5
## Action_13_CTA Positive_14a_Number Negative_14b_Number Individual_14c_Number
## <dbl> <dbl> <dbl> <dbl>
## 1 1 0 0 0
## 2 1 0 0 0
## 3 1 0 0 0
## 4 1 0 0 0
## 5 1 0 0 0
## 6 1 0 0 0
## 7 1 0 0 0
## 8 1 0 0 0
## 9 1 0 0 0
## 10 1 1 0 0
## # … with 9,857 more rows, and 1 more variable: Collective_14d_Number <dbl>correlation(p5)## # Correlation Matrix (pearson-method)
##
## Parameter1 | Parameter2 | r | 95% CI | t(9865) | p
## ----------------------------------------------------------------------------------------------
## Action_13_CTA | Positive_14a_Number | 0.06 | [ 0.04, 0.08] | 6.35 | < .001***
## Action_13_CTA | Negative_14b_Number | 6.12e-03 | [-0.01, 0.03] | 0.61 | > .999
## Action_13_CTA | Individual_14c_Number | 0.03 | [ 0.01, 0.05] | 3.35 | 0.003**
## Action_13_CTA | Collective_14d_Number | 0.05 | [ 0.03, 0.07] | 5.36 | < .001***
## Positive_14a_Number | Negative_14b_Number | 0.02 | [ 0.01, 0.04] | 2.48 | 0.040*
## Positive_14a_Number | Individual_14c_Number | 0.52 | [ 0.50, 0.53] | 60.01 | < .001***
## Positive_14a_Number | Collective_14d_Number | 0.83 | [ 0.83, 0.84] | 149.60 | < .001***
## Negative_14b_Number | Individual_14c_Number | 0.21 | [ 0.19, 0.23] | 21.49 | < .001***
## Negative_14b_Number | Collective_14d_Number | 0.13 | [ 0.11, 0.15] | 12.73 | < .001***
## Individual_14c_Number | Collective_14d_Number | 6.26e-03 | [-0.01, 0.03] | 0.62 | > .999
##
## p-value adjustment method: Holm (1979)
## Observations: 9867rmat_1 <- correlation(p5)
plot(summary(rmat_1))
Total effects
fita_0 <- lm(Action_13_CTA ~ Positive_14a_Number , data = p5 )
fita_0##
## Call:
## lm(formula = Action_13_CTA ~ Positive_14a_Number, data = p5)
##
## Coefficients:
## (Intercept) Positive_14a_Number
## 1.0485 0.0634summary (fita_0)##
## Call:
## lm(formula = Action_13_CTA ~ Positive_14a_Number, data = p5)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1753 -0.1119 -0.0485 -0.0485 15.9515
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.048487 0.011550 90.777 < 2e-16 ***
## Positive_14a_Number 0.063399 0.009985 6.349 2.26e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9627 on 9865 degrees of freedom
## Multiple R-squared: 0.00407, Adjusted R-squared: 0.003969
## F-statistic: 40.31 on 1 and 9865 DF, p-value: 2.259e-10fita_1 <- lm(Action_13_CTA ~ Negative_14b_Number, data = p5 )
fita_1##
## Call:
## lm(formula = Action_13_CTA ~ Negative_14b_Number, data = p5)
##
## Coefficients:
## (Intercept) Negative_14b_Number
## 1.08777 0.03012summary (fita_1)##
## Call:
## lm(formula = Action_13_CTA ~ Negative_14b_Number, data = p5)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.0878 -0.0878 -0.0878 -0.0878 15.9122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.087768 0.009763 111.418 <2e-16 ***
## Negative_14b_Number 0.030115 0.049554 0.608 0.543
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9647 on 9865 degrees of freedom
## Multiple R-squared: 3.744e-05, Adjusted R-squared: -6.393e-05
## F-statistic: 0.3693 on 1 and 9865 DF, p-value: 0.5434fita_2<- lm(Action_13_CTA ~ Individual_14c_Number, data = p5 )
fita_2##
## Call:
## lm(formula = Action_13_CTA ~ Individual_14c_Number, data = p5)
##
## Coefficients:
## (Intercept) Individual_14c_Number
## 1.07751 0.06016summary (fita_2)##
## Call:
## lm(formula = Action_13_CTA ~ Individual_14c_Number, data = p5)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1377 -0.0775 -0.0775 -0.0775 15.9225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.07751 0.01023 105.292 < 2e-16 ***
## Individual_14c_Number 0.06016 0.01795 3.351 0.000808 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9642 on 9865 degrees of freedom
## Multiple R-squared: 0.001137, Adjusted R-squared: 0.001036
## F-statistic: 11.23 on 1 and 9865 DF, p-value: 0.0008081fita_3 <- lm(Action_13_CTA ~ Collective_14d_Number, data = p5 )
fita_3##
## Call:
## lm(formula = Action_13_CTA ~ Collective_14d_Number, data = p5)
##
## Coefficients:
## (Intercept) Collective_14d_Number
## 1.05907 0.06246summary (fita_3)##
## Call:
## lm(formula = Action_13_CTA ~ Collective_14d_Number, data = p5)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1840 -0.1215 -0.0591 -0.0591 15.9409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.05907 0.01113 95.13 < 2e-16 ***
## Collective_14d_Number 0.06246 0.01165 5.36 8.51e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9633 on 9865 degrees of freedom
## Multiple R-squared: 0.002904, Adjusted R-squared: 0.002803
## F-statistic: 28.73 on 1 and 9865 DF, p-value: 8.51e-08library(performance)
compare_performance(fita_0, fita_1, fita_2, fita_3, metrics = c("R2", "R2_adj"))## # Comparison of Model Performance Indices
##
## Name | Model | R2 | R2 (adj.)
## ---------------------------------------
## fita_0 | lm | 0.004 | 0.004
## fita_1 | lm | 3.744e-05 | -6.393e-05
## fita_2 | lm | 0.001 | 0.001
## fita_3 | lm | 0.003 | 0.003Partial effects
fit1_0 <- lm(Action_13_CTA ~ Positive_14a_Number + Negative_14b_Number, data = p5 )
fit1_0##
## Call:
## lm(formula = Action_13_CTA ~ Positive_14a_Number + Negative_14b_Number,
## data = p5)
##
## Coefficients:
## (Intercept) Positive_14a_Number Negative_14b_Number
## 1.04811 0.06329 0.02230parameters(fit1_0)## Parameter | Coefficient | SE | 95% CI | t(9864) | p
## -------------------------------------------------------------------------------
## (Intercept) | 1.05 | 0.01 | [ 1.03, 1.07] | 90.50 | < .001
## Positive 14a Number | 0.06 | 9.99e-03 | [ 0.04, 0.08] | 6.34 | < .001
## Negative 14b Number | 0.02 | 0.05 | [-0.07, 0.12] | 0.45 | 0.652##
## Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
## using a Wald t-distribution approximation.performance(fit1_0)## # Indices of model performance
##
## AIC | BIC | R2 | R2 (adj.) | RMSE | Sigma
## ---------------------------------------------------------
## 27257.809 | 27286.597 | 0.004 | 0.004 | 0.963 | 0.963plot(ggpredict(fit1_0, terms = c("Positive_14a_Number", "Negative_14b_Number[1,3,6]")))
fit1_1 <- lm(Action_13_CTA ~ Individual_14c_Number + Collective_14d_Number, data = p5 )
fit1_1##
## Call:
## lm(formula = Action_13_CTA ~ Individual_14c_Number + Collective_14d_Number,
## data = p5)
##
## Coefficients:
## (Intercept) Individual_14c_Number Collective_14d_Number
## 1.04842 0.05956 0.06222parameters(fit1_1)## Parameter | Coefficient | SE | 95% CI | t(9864) | p
## ----------------------------------------------------------------------------
## (Intercept) | 1.05 | 0.01 | [1.03, 1.07] | 90.54 | < .001
## Individual 14c Number | 0.06 | 0.02 | [0.02, 0.09] | 3.32 | < .001
## Collective 14d Number | 0.06 | 0.01 | [0.04, 0.09] | 5.34 | < .001##
## Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
## using a Wald t-distribution approximation.performance(fit1_1)## # Indices of model performance
##
## AIC | BIC | R2 | R2 (adj.) | RMSE | Sigma
## ---------------------------------------------------------
## 27258.524 | 27287.312 | 0.004 | 0.004 | 0.963 | 0.963plot(ggpredict(fit1_1, terms = c("Individual_14c_Number", "Collective_14d_Number[2,4,8]")))
Simple effects
fit1_1 <- lm(Action_13_CTA ~ Positive_14a_Number * Negative_14b_Number, data = p5 )
fit1_1##
## Call:
## lm(formula = Action_13_CTA ~ Positive_14a_Number * Negative_14b_Number,
## data = p5)
##
## Coefficients:
## (Intercept)
## 1.04970
## Positive_14a_Number
## 0.06068
## Negative_14b_Number
## -0.06110
## Positive_14a_Number:Negative_14b_Number
## 0.09899parameters(fit1_1)## Parameter | Coefficient | SE | 95% CI | t(9863) | p
## -------------------------------------------------------------------------------------------------
## (Intercept) | 1.05 | 0.01 | [ 1.03, 1.07] | 90.48 | < .001
## Positive 14a Number | 0.06 | 0.01 | [ 0.04, 0.08] | 6.03 | < .001
## Negative 14b Number | -0.06 | 0.06 | [-0.18, 0.06] | -0.98 | 0.328
## Positive 14a Number * Negative 14b Number | 0.10 | 0.05 | [ 0.01, 0.19] | 2.19 | 0.029##
## Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
## using a Wald t-distribution approximation.performance(fit1_1)## # Indices of model performance
##
## AIC | BIC | R2 | R2 (adj.) | RMSE | Sigma
## ---------------------------------------------------------
## 27255.034 | 27291.019 | 0.005 | 0.004 | 0.962 | 0.963plot(ggpredict(fit1_1, terms = c("Positive_14a_Number", "Negative_14b_Number[1,3,6]")))
fit_check1_1 <- check_model(fit1_1)
fit_check1_1
fit1_2 <- lm(Action_13_CTA ~ Individual_14c_Number * Collective_14d_Number, data = p5 )
fit1_2##
## Call:
## lm(formula = Action_13_CTA ~ Individual_14c_Number * Collective_14d_Number,
## data = p5)
##
## Coefficients:
## (Intercept)
## 1.04750
## Individual_14c_Number
## 0.06512
## Collective_14d_Number
## 0.06464
## Individual_14c_Number:Collective_14d_Number
## -0.01387parameters(fit1_2)## Parameter | Coefficient | SE | 95% CI | t(9863) | p
## -----------------------------------------------------------------------------------------------------
## (Intercept) | 1.05 | 0.01 | [ 1.02, 1.07] | 89.68 | < .001
## Individual 14c Number | 0.07 | 0.02 | [ 0.03, 0.10] | 3.23 | 0.001
## Collective 14d Number | 0.06 | 0.01 | [ 0.04, 0.09] | 5.25 | < .001
## Individual 14c Number * Collective 14d Number | -0.01 | 0.02 | [-0.06, 0.03] | -0.61 | 0.545##
## Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
## using a Wald t-distribution approximation.performance(fit1_2)## # Indices of model performance
##
## AIC | BIC | R2 | R2 (adj.) | RMSE | Sigma
## ---------------------------------------------------------
## 27260.157 | 27296.142 | 0.004 | 0.004 | 0.963 | 0.963plot(ggpredict(fit1_2, terms = c("Individual_14c_Number", "Collective_14d_Number[2,4,8]")))
fit_check1_2 <- check_model(fit1_2)
fit_check1_2
#https://stats.oarc.ucla.edu/r/dae/zinb/
require(ggplot2)
require(pscl)## Loading required package: pscl## Classes and Methods for R developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University
## Simon Jackman
## hurdle and zeroinfl functions by Achim Zeileisrequire(MASS)## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectrequire(boot)## Loading required package: bootlibrary(ggplot2)
library(pscl)
library(MASS)
library(boot)require(nonnest2)## Loading required package: nonnest2## This is nonnest2 0.5-5.
## nonnest2 has not been tested with all combinations of model classes.require(lmtest)## Loading required package: lmtest## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericp2## # A tibble: 9,867 × 29
## Post_oldid Real_ID date COVID_Post RiskFactor_2_CTA SocialDisparities_3_…
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 1 2069 9/22/20 1 0 0
## 2 2 2070 8/27/20 1 0 0
## 3 3 2071 12/29/20 1 0 0
## 4 4 2072 9/29/20 1 0 0
## 5 5 2073 10/6/20 1 0 0
## 6 6 2074 12/31/20 1 0 0
## 7 7 403 4/2/20 1 0 0
## 8 8 5425 12/1/20 1 0 0
## 9 9 5842 12/20/20 1 0 0
## 10 10 2075 3/5/20 1 0 0
## # … with 9,857 more rows, and 23 more variables: Debunk_4_CTA <dbl>,
## # UncertaintyReduction_5_CTA <dbl>, Testing_6_CTA <dbl>, Vaccine_7_CTA <dbl>,
## # Action_13_CTA <dbl>, Positive_14a_Number <dbl>, Negative_14b_Number <dbl>,
## # Individual_14c_Number <dbl>, Collective_14d_Number <dbl>,
## # `State_ recode` <dbl>, Gov_poli <fct>, Trifecta <fct>,
## # `White_\bpercent` <dbl>, minority_percent <dbl>, Poverty_percent <dbl>,
## # ProspertiyRanking <dbl>, year <dbl>, month <dbl>, day <dbl>, state <chr>, …colnames(p2)## [1] "Post_oldid" "Real_ID"
## [3] "date" "COVID_Post"
## [5] "RiskFactor_2_CTA" "SocialDisparities_3_CTA"
## [7] "Debunk_4_CTA" "UncertaintyReduction_5_CTA"
## [9] "Testing_6_CTA" "Vaccine_7_CTA"
## [11] "Action_13_CTA" "Positive_14a_Number"
## [13] "Negative_14b_Number" "Individual_14c_Number"
## [15] "Collective_14d_Number" "State_ recode"
## [17] "Gov_poli" "Trifecta"
## [19] "White_\bpercent" "minority_percent"
## [21] "Poverty_percent" "ProspertiyRanking"
## [23] "year" "month"
## [25] "day" "state"
## [27] "cases" "efficacy_total"
## [29] "efficacy_total1"###Zero inflated models
#Positive_14a_Number vs. RiskFactor_2_CTA (contibous vs. categories)
m1_01 <- zeroinfl(Positive_14a_Number ~ RiskFactor_2_CTA,
data = p2, dist = "negbin")
summary(m1_01)##
## Call:
## zeroinfl(formula = Positive_14a_Number ~ RiskFactor_2_CTA, data = p2,
## dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -0.7457 -0.6428 -0.6428 0.4450 8.0592
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.48069 0.04476 -10.738 < 2e-16 ***
## RiskFactor_2_CTA 0.34364 0.05782 5.944 2.79e-09 ***
## Log(theta) 0.42858 0.10643 4.027 5.65e-05 ***
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.0697 0.9375 -3.274 0.00106 **
## RiskFactor_2_CTA -8.2829 44.7778 -0.185 0.85325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 1.5351
## Number of iterations in BFGS optimization: 34
## Log-likelihood: -1.066e+04 on 5 Df#Positive_14a_Number ~ minority_percent (continous vs. contious)
m1_0 <- zeroinfl(Positive_14a_Number ~ minority_percent,
data = p2, dist = "negbin")
summary(m1_0)##
## Call:
## zeroinfl(formula = Positive_14a_Number ~ minority_percent, data = p2,
## dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -0.7373 -0.6909 -0.5453 0.3514 9.1417
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.151271 0.068852 -2.197 0.0280 *
## minority_percent -0.002513 0.001272 -1.976 0.0482 *
## Log(theta) 0.935203 0.116985 7.994 1.3e-15 ***
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.68499 0.15625 4.384 1.17e-05 ***
## minority_percent -0.06610 0.00613 -10.784 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 2.5477
## Number of iterations in BFGS optimization: 21
## Log-likelihood: -1.062e+04 on 5 Dfm1_1 <- zeroinfl(Negative_14b_Number ~ minority_percent,
data = p2, dist = "negbin")
summary(m1_1)##
## Call:
## zeroinfl(formula = Negative_14b_Number ~ minority_percent, data = p2,
## dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -0.11448 -0.10698 -0.10327 -0.09904 30.80180
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.93796 1.20766 -1.605 0.109
## minority_percent 0.01666 0.01022 1.629 0.103
## Log(theta) -0.75055 1.69809 -0.442 0.658
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.411832 1.292669 1.866 0.0621 .
## minority_percent 0.003307 0.009709 0.341 0.7334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 0.4721
## Number of iterations in BFGS optimization: 21
## Log-likelihood: -849.2 on 5 Dfm1_2 <- zeroinfl(Individual_14c_Number ~ minority_percent,
data = p2, dist = "negbin")## Warning in value[[3L]](cond): system is computationally singular: reciprocal
## condition number = 8.26018e-19FALSEsummary(m1_2)##
## Call:
## zeroinfl(formula = Individual_14c_Number ~ minority_percent, data = p2,
## dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -0.3566 -0.3482 -0.3434 -0.3339 12.0980
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.579088 NA NA NA
## minority_percent -0.003602 NA NA NA
## Log(theta) -1.058116 NA NA NA
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.481 NA NA NA
## minority_percent -3.157 NA NA NA
##
## Theta = 0.3471
## Number of iterations in BFGS optimization: 28
## Log-likelihood: -4907 on 5 Dfm1_2 <- zeroinfl(Collective_14d_Number ~ minority_percent,
data = p2, dist = "negbin")
summary(m1_2)##
## Call:
## zeroinfl(formula = Collective_14d_Number ~ minority_percent, data = p2,
## dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -0.6737 -0.6156 -0.4899 0.5212 12.8568
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.396547 0.088159 -4.498 6.86e-06 ***
## minority_percent -0.002061 0.001580 -1.304 0.192
## Log(theta) 0.846818 0.129362 6.546 5.90e-11 ***
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.381123 0.158324 8.723 <2e-16 ***
## minority_percent -0.078370 0.006336 -12.368 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 2.3322
## Number of iterations in BFGS optimization: 22
## Log-likelihood: -8943 on 5 Df###Risk factor vs. efficacy
#??? Is this the interactions? How to interprete that.
m1 <- zeroinfl(Action_13_CTA ~ efficacy_total|RiskFactor_2_CTA,
data = p2, dist = "negbin")## Warning in sqrt(diag(vc)[np]): NaNs producedsummary(m1)##
## Call:
## zeroinfl(formula = Action_13_CTA ~ efficacy_total | RiskFactor_2_CTA,
## data = p2, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -1.08063 -0.10191 -0.04865 -0.04865 15.56690
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.048643 0.011609 4.190 2.79e-05 ***
## efficacy_total 0.053225 0.009144 5.821 5.85e-09 ***
## Log(theta) 16.322734 NaN NaN NaN
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -16.413 40.112 -0.409 0.682
## RiskFactor_2_CTA -1.369 203.184 -0.007 0.995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 12270811.5686
## Number of iterations in BFGS optimization: 46
## Log-likelihood: -1.144e+04 on 5 Dfm2 <- zeroinfl(Action_13_CTA ~ efficacy_total1|RiskFactor_2_CTA,
data = p2, dist = "negbin")
summary(m2)##
## Call:
## zeroinfl(formula = Action_13_CTA ~ efficacy_total1 | RiskFactor_2_CTA,
## data = p2, dist = "negbin")
##
## Pearson residuals:
## Min 1Q Median 3Q Max
## -1.08068 -0.10195 -0.04863 -0.04863 15.56703
##
## Count model coefficients (negbin with log link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.048629 0.011607 4.190 2.79e-05 ***
## efficacy_total1 0.053278 0.009145 5.826 5.68e-09 ***
## Log(theta) 18.067151 1.712059 10.553 < 2e-16 ***
##
## Zero-inflation model coefficients (binomial with logit link):
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -17.553 70.941 -0.247 0.805
## RiskFactor_2_CTA -1.322 351.334 -0.004 0.997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Theta = 70220529.8648
## Number of iterations in BFGS optimization: 68
## Log-likelihood: -1.144e+04 on 5 Df#DOH_uncetainty <- p %>%
# filter(UncertaintyReduction_5_CTA != 88)
# DOH_uncetainty#DOH_no88 <- p[p[,"RiskFactor_2_CTA"] != 88 |
# p[,"SocialDisparities_3_CTA"] != 88 |
# p[,"Debunk_4_CTA"] != 88 |
# p[,"UncertaintyReduction_5_CTA"] != 88 |
# p[,"Testing_6_CTA"] !=88 |
# p[,"Vaccine_7_CTA"]!= 88,
# ]
#
# # DOH_no88 # RiskFactor_2_CTA
# SocialDisparities_3_CTA
# Debunk_4_CTA
# UncertaintyReduction_5_CTA
# Testing_6_CTA
# Vaccine_7_CTA
# a#a <- p[p$RiskFactor_2_CTA > 0 & p$SocialDisparities_3_CT > 0,
# p$Debunk_4_CTA > 0 & p$UncertaintyReduction_5_CTA > 0,
# p$Testing_6_CTA > 0 & p$Vaccine_7_CTA > 0,
# ]
#a library("dplyr")
# a <- filter(p, RiskFactor_2_CTA != 88)
# b <- filter(p, SocialDisparities_3_CTA != 88)
# c <- filter(p, Debunk_4_CTA != 88)
# d <- filter(p, UncertaintyReduction_5_CTA != 88)
# e <- filter(p, Testing_6_CTA != 88)
# f <- filter(p, Vaccine_7_CTA != 88)
# k <- filter(p, Action_13_CTA != 88)
# a
# b
# c
# d
# e
# f # DOH_no88 <- rbind(a, b, c, d, e,f)
# DOH_no88